source('../settings/settings.R')
source('commonFunctions.R')
inputFileDrive1 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=1, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive2 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=2, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive3 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=3, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive4 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=4, distPrev=30, distNext=30))
drive1 <- read.csv(inputFileDrive1)
drive2 <- read.csv(inputFileDrive2)
drive3 <- read.csv(inputFileDrive3)
drive4 <- read.csv(inputFileDrive4, stringsAsFactors = T)
set.seed(43)
combinedDf <- cbind(drive4,
drive1$MeanPP_Seg0,
drive2$MeanPP, drive3$MeanPP,
drive2$StdPP, drive3$StdPP,
drive2$MeanPP_SegMax, drive3$MeanPP_SegMax,
drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
drive2$StdPP_SegMax, drive3$StdPP_SegMax,
drive2$StdPP_Seg0, drive3$StdPP_Seg0,
drive2$MeanPP_AccHigh, drive3$MeanPP_AccHigh,
drive2$X.MeanPP_AccLow, drive3$X.MeanPP_AccLow,
drive2$StdPP_AccHigh, drive3$StdPP_AccHigh,
drive2$StdPP_AccLow, drive3$StdPP_AccLow
)
names(combinedDf) <- c(names(drive4),
"PP_Dev_1_Turning",
"PP_Dev_2", "PP_Dev_3",
"Std_PP_2", "Std_PP_3",
"PP_Dev_2_Straight", "PP_Dev_3_Straight",
"PP_Dev_2_Turning", "PP_Dev_3_Turning",
"Std_PP_2_Straight", "Std_PP_3_Straight",
"Std_PP_2_Turning", "Std_PP_3_Turning",
"Mean_PP_2_AccHigh", "Mean_PP_3_AccHigh",
"Mean_PP_2_AccLow", "Mean_PP_3_AccLow",
"Std_PP_2_AccHigh", "Std_PP_3_AccHigh",
"Std_PP_2_AccLow", "Std_PP_3_AccLow"
)
combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))
combinedDf$ActivityEncoded <- factor(ifelse(combinedDf$Activity == "NO", "1", ifelse(combinedDf$Activity == "C", "2", "3")))
# combinedDf$PP_Dev_2_Turning <- ifelse(combinedDf$PP_Dev_2_Turning > 0, combinedDf$PP_Dev_2_Turning, combinedDf$PP_Dev_2_Straight)
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]
combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE_PRIOR <- list(color='green')
COLOR_FAILURE <- list(color='red')
COLOR_COGNITIVE_ACC <- list(color='rgb(158,202,225)')
COLOR_MOTORIC_ACC <- list(color='rgb(58,200,225)')
bargap <- 6
yAxis <- list(
title = "Arousal ΔPP [ln°C²]",
range=c(-0.2, 0.6)
)
# Apply Otsu algorithm to select threshold
ppDev <- combinedDf$PP_After # PP_Dev
ppDevArray <- matrix(ppDev, nrow = 1,ncol = length(ppDev))
THRESHOLD_MILD = otsu(ppDevArray, range=c(min(ppDev), max(ppDev))) # Expected Threshold > 0.042
print(paste0('Threshold: ', THRESHOLD_MILD))
[1] "Threshold: 0.101235546875"
MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
xAxis = list(
title = "Subject",
ticktext = combinedDf_NoStressor$Subject,
tickvals = seq(1, bargap * nrow(combinedDf_NoStressor), by=bargap),
tickmode = "array"
)
combinedDf_NoStressor$SubjectLevel <- seq(1, bargap * nrow(combinedDf_NoStressor), by=bargap)
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~SubjectLevel, width=1000) %>%
# add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>%
# add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
# add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>%
# add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>%
add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F)
fig_NoStressor <- fig_NoStressor %>% config(mathjax = 'cdn')
htmltools::tagList(fig_NoStressor)
xAxis = list(
title = "Subject",
ticktext = combinedDf_Cognitive$Subject,
tickvals = seq(1, bargap * nrow(combinedDf_Cognitive), by=bargap),
tickmode = "array"
)
combinedDf_Cognitive$SubjectLevel <- seq(1, bargap * nrow(combinedDf_Cognitive), by=bargap)
fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~SubjectLevel, width=1000) %>%
# add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>%
# add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
# add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>%
# add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>%
add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F)
htmltools::tagList(fig_Cognitive)
xAxis = list(
title = "Subject",
ticktext = combinedDf_Motoric$Subject,
tickvals = seq(1, bargap * nrow(combinedDf_Motoric), by=bargap),
tickmode = "array"
)
combinedDf_Motoric$SubjectLevel <- seq(1, bargap * nrow(combinedDf_Motoric), by=bargap)
fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~SubjectLevel, width=1000) %>%
# add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
# add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>%
# add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
# add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>%
# add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
# add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>%
add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F)
htmltools::tagList(fig_Motoric)
library(nlme)
combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)
combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
Linear model with all variables
linearModel1 <- lm(PP_After ~
+ PP_Dev_2_Straight
+ PP_Dev_3_Straight
+ PP_Dev_2_Turning
+ PP_Dev_3_Turning
+ Std_PP_2_Straight
+ Std_PP_3_Straight
+ Std_PP_2_Turning
+ Std_PP_3_Turning
+ PP_Prior
+ factor(ActivityEncoded),
data=combinedDf)
# anova(model)
summary(linearModel1)
Call:
lm(formula = PP_After ~ +PP_Dev_2_Straight + PP_Dev_3_Straight +
PP_Dev_2_Turning + PP_Dev_3_Turning + Std_PP_2_Straight +
Std_PP_3_Straight + Std_PP_2_Turning + Std_PP_3_Turning +
PP_Prior + factor(ActivityEncoded), data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.076664 -0.027277 -0.000867 0.021999 0.100001
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.07132 0.08729 -0.817 0.4350
PP_Dev_2_Straight 0.71569 0.36574 1.957 0.0821 .
PP_Dev_3_Straight -0.81109 0.44442 -1.825 0.1013
PP_Dev_2_Turning -0.55309 0.44548 -1.242 0.2458
PP_Dev_3_Turning 0.66117 0.46801 1.413 0.1914
Std_PP_2_Straight 1.41894 1.36691 1.038 0.3263
Std_PP_3_Straight 1.27101 0.71461 1.779 0.1090
Std_PP_2_Turning -1.53955 1.68233 -0.915 0.3840
Std_PP_3_Turning 0.16636 1.14505 0.145 0.8877
PP_Prior 0.70952 0.25103 2.826 0.0198 *
factor(ActivityEncoded)2 0.04926 0.07400 0.666 0.5224
factor(ActivityEncoded)3 0.12239 0.05641 2.170 0.0582 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0682 on 9 degrees of freedom
Multiple R-squared: 0.9037, Adjusted R-squared: 0.7859
F-statistic: 7.675 on 11 and 9 DF, p-value: 0.002452
plot(linearModel1)




linearModel1 <- lm(PP_After ~
Mean_PP_2_AccHigh
+ Mean_PP_2_AccLow
+ Mean_PP_3_AccHigh
+ Mean_PP_3_AccLow
+ Std_PP_2_AccHigh
+ Std_PP_2_AccLow
+ Std_PP_3_AccHigh
+ Std_PP_3_AccLow
# + PP_Prior
+ factor(ActivityEncoded),
data=combinedDf)
# anova(model)
summary(linearModel1)
Call:
lm(formula = PP_After ~ Mean_PP_2_AccHigh + Mean_PP_2_AccLow +
Mean_PP_3_AccHigh + Mean_PP_3_AccLow + Std_PP_2_AccHigh +
Std_PP_2_AccLow + Std_PP_3_AccHigh + Std_PP_3_AccLow + factor(ActivityEncoded),
data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.110015 -0.048043 0.009167 0.036551 0.082263
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.36297 0.09880 -3.674 0.00429 **
Mean_PP_2_AccHigh 2.14482 0.63161 3.396 0.00682 **
Mean_PP_2_AccLow -1.74610 0.63007 -2.771 0.01974 *
Mean_PP_3_AccHigh 2.99003 0.78293 3.819 0.00338 **
Mean_PP_3_AccLow -2.40638 0.75184 -3.201 0.00948 **
Std_PP_2_AccHigh 5.27168 4.04124 1.304 0.22130
Std_PP_2_AccLow -4.34469 2.79301 -1.556 0.15087
Std_PP_3_AccHigh 0.67278 1.84262 0.365 0.72262
Std_PP_3_AccLow 3.14657 2.14416 1.468 0.17297
factor(ActivityEncoded)2 0.18930 0.05019 3.771 0.00365 **
factor(ActivityEncoded)3 0.13988 0.05308 2.635 0.02494 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07638 on 10 degrees of freedom
Multiple R-squared: 0.8657, Adjusted R-squared: 0.7315
F-statistic: 6.449 on 10 and 10 DF, p-value: 0.003428
plot(linearModel1)




With Prior
linearModelWPrior <- lm(PP_After ~
Mean_PP_2_AccHigh
+ Mean_PP_2_AccLow
+ Mean_PP_3_AccHigh
+ Mean_PP_3_AccLow
+ Std_PP_2_AccHigh
+ Std_PP_2_AccLow
+ Std_PP_3_AccHigh
+ Std_PP_3_AccLow
+ PP_Prior
+ factor(ActivityEncoded),
data=combinedDf)
# anova(model)
summary(linearModelWPrior)
Call:
lm(formula = PP_After ~ Mean_PP_2_AccHigh + Mean_PP_2_AccLow +
Mean_PP_3_AccHigh + Mean_PP_3_AccLow + Std_PP_2_AccHigh +
Std_PP_2_AccLow + Std_PP_3_AccHigh + Std_PP_3_AccLow + PP_Prior +
factor(ActivityEncoded), data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.067676 -0.021534 -0.007049 0.015917 0.088012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.26129 0.07583 -3.446 0.00733 **
Mean_PP_2_AccHigh 1.63612 0.46949 3.485 0.00689 **
Mean_PP_2_AccLow -1.35111 0.45858 -2.946 0.01632 *
Mean_PP_3_AccHigh 1.63055 0.68413 2.383 0.04100 *
Mean_PP_3_AccLow -1.46332 0.59910 -2.443 0.03721 *
Std_PP_2_AccHigh 6.65712 2.87230 2.318 0.04566 *
Std_PP_2_AccLow -4.05512 1.96633 -2.062 0.06922 .
Std_PP_3_AccHigh -0.99942 1.38885 -0.720 0.49003
Std_PP_3_AccLow 3.57312 1.51344 2.361 0.04254 *
PP_Prior 0.63520 0.18967 3.349 0.00854 **
factor(ActivityEncoded)2 0.12025 0.04088 2.942 0.01645 *
factor(ActivityEncoded)3 0.15861 0.03775 4.202 0.00230 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05372 on 9 degrees of freedom
Multiple R-squared: 0.9402, Adjusted R-squared: 0.8672
F-statistic: 12.87 on 11 and 9 DF, p-value: 0.00033
plot(linearModelWPrior)



NaNs producedNaNs produced

linearModel3 <- lm(PP_After ~
Mean_PP_2_AccHigh
+ Mean_PP_2_AccLow
+ Mean_PP_3_AccHigh
+ Mean_PP_3_AccLow
# + PP_Prior
+ factor(ActivityEncoded),
data=combinedDf)
# anova(model)
summary(linearModel3)
Call:
lm(formula = PP_After ~ Mean_PP_2_AccHigh + Mean_PP_2_AccLow +
Mean_PP_3_AccHigh + Mean_PP_3_AccLow + factor(ActivityEncoded),
data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.135285 -0.049920 -0.003805 0.045609 0.123392
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.21802 0.06604 -3.301 0.00525 **
Mean_PP_2_AccHigh 2.11765 0.65809 3.218 0.00620 **
Mean_PP_2_AccLow -1.58805 0.65839 -2.412 0.03016 *
Mean_PP_3_AccHigh 2.97984 0.73529 4.053 0.00119 **
Mean_PP_3_AccLow -2.53203 0.69956 -3.619 0.00279 **
factor(ActivityEncoded)2 0.17285 0.05284 3.271 0.00557 **
factor(ActivityEncoded)3 0.16446 0.04579 3.592 0.00295 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.08212 on 14 degrees of freedom
Multiple R-squared: 0.7828, Adjusted R-squared: 0.6896
F-statistic: 8.407 on 6 and 14 DF, p-value: 0.0005398
plot(linearModel3)




# Export the anova table
library(xtable)
lmCoeffs <- summary(linearModel1)$coefficients
lmAnova <- anova(linearModel1)
print(xtable(lmCoeffs, digits=c(0,5,5,5,5)))
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Sat Jul 11 19:08:30 2020
\begin{table}[ht]
\centering
\begin{tabular}{rrrrr}
\hline
& Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\
\hline
(Intercept) & -0.36297 & 0.09880 & -3.67377 & 0.00429 \\
Mean\_PP\_2\_AccHigh & 2.14482 & 0.63161 & 3.39577 & 0.00682 \\
Mean\_PP\_2\_AccLow & -1.74610 & 0.63007 & -2.77127 & 0.01974 \\
Mean\_PP\_3\_AccHigh & 2.99003 & 0.78293 & 3.81901 & 0.00338 \\
Mean\_PP\_3\_AccLow & -2.40638 & 0.75184 & -3.20067 & 0.00948 \\
Std\_PP\_2\_AccHigh & 5.27168 & 4.04124 & 1.30447 & 0.22130 \\
Std\_PP\_2\_AccLow & -4.34469 & 2.79301 & -1.55556 & 0.15087 \\
Std\_PP\_3\_AccHigh & 0.67278 & 1.84262 & 0.36512 & 0.72262 \\
Std\_PP\_3\_AccLow & 3.14657 & 2.14416 & 1.46750 & 0.17297 \\
factor(ActivityEncoded)2 & 0.18930 & 0.05019 & 3.77139 & 0.00365 \\
factor(ActivityEncoded)3 & 0.13988 & 0.05308 & 2.63525 & 0.02494 \\
\hline
\end{tabular}
\end{table}
print(xtable(lmAnova), digits=c(0,5,5,5,5))
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Sat Jul 11 19:08:30 2020
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrr}
\hline
& Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\
\hline
Mean\_PP\_2\_AccHigh & 1 & 0.15 & 0.15 & 26.27 & 0.0004 \\
Mean\_PP\_2\_AccLow & 1 & 0.00 & 0.00 & 0.32 & 0.5845 \\
Mean\_PP\_3\_AccHigh & 1 & 0.01 & 0.01 & 1.09 & 0.3207 \\
Mean\_PP\_3\_AccLow & 1 & 0.06 & 0.06 & 11.06 & 0.0077 \\
Std\_PP\_2\_AccHigh & 1 & 0.00 & 0.00 & 0.06 & 0.8116 \\
Std\_PP\_2\_AccLow & 1 & 0.00 & 0.00 & 0.50 & 0.4940 \\
Std\_PP\_3\_AccHigh & 1 & 0.03 & 0.03 & 5.02 & 0.0490 \\
Std\_PP\_3\_AccLow & 1 & 0.02 & 0.02 & 3.14 & 0.1070 \\
factor(ActivityEncoded) & 2 & 0.10 & 0.05 & 8.51 & 0.0069 \\
Residuals & 10 & 0.06 & 0.01 & & \\
\hline
\end{tabular}
\end{table}
ppAfter <- combinedDf$PP_After
ppAfterArray <- matrix(ppAfter, nrow = 1,ncol = length(ppAfter))
thresholdPPAfter <- otsu(ppAfterArray, range=c(min(ppAfter), max(ppAfter))) # Expected Threshold > 0.042
print(paste0('Threshold: ', thresholdPPAfter))
[1] "Threshold: 0.101235546875"
selectedDf <- combinedDf %>% select(
"Subject", "Activity", "PP_After", "PP_Prior",
"Mean_PP_2_AccHigh", "Mean_PP_3_AccHigh",
"Mean_PP_2_AccLow", "Mean_PP_3_AccLow",
"Std_PP_2_AccHigh", "Std_PP_3_AccHigh",
"Std_PP_2_AccLow", "Std_PP_3_AccLow")
selectedDf$Subject <- NULL
selectedDf$Activity_NO <- ifelse(selectedDf$Activity == "NO", 1, 0)
selectedDf$Activity_C <- ifelse(selectedDf$Activity == "C", 1, 0)
selectedDf$Activity_M <- ifelse(selectedDf$Activity == "M", 1, 0)
selectedDf$Activity <- NULL
# selectedDf$PP_Dev_1_Turning <- NULL
# selectedDf$Std_PP_2_Straight <- NULL
# selectedDf$Std_PP_2_Turning <- NULL
# selectedDf$Std_PP_3_Straight <- NULL
# selectedDf$Std_PP_3_Turning <- NULL
#
# # According to Linear model
# selectedDf$PP_Dev_2_Straight <- abs(selectedDf$PP_Dev_2_Straight)
# selectedDf$PP_Dev_3_Straight <- abs(selectedDf$PP_Dev_3_Straight)
# selectedDf$PP_Dev_2_Turning <- abs(selectedDf$PP_Dev_2_Turning)
# selectedDf$PP_Dev_3_Turning <- abs(selectedDf$PP_Dev_3_Turning)
# selectedDf$PP_Prior <- abs(selectedDf$PP_Prior) # NULL
selectedDf$Class <- ifelse(selectedDf$PP_After >= thresholdPPAfter, T, F)
selectedDf$PP_After <- NULL
print(names(selectedDf))
[1] "PP_Prior" "Mean_PP_2_AccHigh" "Mean_PP_3_AccHigh" "Mean_PP_2_AccLow" "Mean_PP_3_AccLow" "Std_PP_2_AccHigh"
[7] "Std_PP_3_AccHigh" "Std_PP_2_AccLow" "Std_PP_3_AccLow" "Activity_NO" "Activity_C" "Activity_M"
[13] "Class"
# library(mefa)
# combinedDf <- rep(combinedDf, 10)
set.seed(43)
n_folds <- 3
params <- param <- list(objective = "binary:logistic",
booster = "gbtree",
eval_metric = "auc",
eta = 0.1,
max_depth = 10,
alpha = 1,
lambda = 0,
gamma = 0.45,
min_child_weight = 0.3,
subsample = 0.5,
colsample_bytree = 1)
# XGBoost Model
xgb_m <- xgb.cv( params = param,
data = as.matrix(selectedDf %>% select(-Class)) ,
label = selectedDf$Class,
nrounds = 100,
verbose = F,
prediction = T,
maximize = F, # Change this value to F will help to run with more itineration
nfold = n_folds,
metrics = c("auc", "error"),
early_stopping_rounds = 50,
stratified = T,
scale_pos_weight = 1)
# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]
NA
Performance Metrics
# Prediction
selectedDf$clsPred <- round(xgb_m$pred)
computePerformanceResults <- function(sdat){
sdat = sdat[complete.cases(sdat),]
acc = sum(sdat[,1] == sdat[,2])/nrow(sdat)
conf_mat = table(sdat)
specif = conf_mat[1,1]/sum(conf_mat[,1])
sensiv = conf_mat[2,2]/sum(conf_mat[,2])
preci = conf_mat[2,2]/sum(conf_mat[2,])
npv = conf_mat[1,1]/sum(conf_mat[1,])
return(c(acc,specif,sensiv,preci,npv))
}
# Get average performance
performance <- computePerformanceResults(selectedDf %>% select(Class, clsPred))
acc <- performance[1]
prec <- performance[4]
recall <- performance[3]
spec <- performance[2]
npv <- performance[5]
f1 <- (2 * recall * prec) / (recall + prec)
auc <- as.numeric(xgb_m$evaluation_log[xgb_m$best_iteration, "test_auc_mean"])
print(paste("Accuracy=", round(acc, 2)))
[1] "Accuracy= 0.71"
print(paste("Precision=", round(prec, 2)))
[1] "Precision= 0.33"
print(paste("Recall=", round(recall, 2)))
[1] "Recall= 1"
print(paste("Specificity=", round(spec, 2)))
[1] "Specificity= 0.67"
print(paste("NPV=", round(npv, 2)))
[1] "NPV= 1"
print(paste("F1=", round(f1, 2)))
[1] "F1= 0.5"
print(paste("AUC=", round(auc, 2)))
[1] "AUC= 0.65"
# Importance
bst <- xgboost( params = param,
data = as.matrix(selectedDf %>% select(-c(Class, clsPred))) ,
label = selectedDf$Class,
nrounds = 100,
verbose = F,
prediction = T,
maximize = F, # Change this value to F will help to run with more itineration
nfold = n_folds,
metrics = c("auc", "error"),
early_stopping_rounds = 50,
stratified = T,
scale_pos_weight = 1)
importanceDf <- xgb.importance(colnames(selectedDf %>% select(-c(Class, clsPred))), model = bst)
print(importanceDf)
library(pROC)
dfROC <- pROC::roc(response = ifelse(selectedDf$Class==T, 1, 0),
predictor = round(xgb_m$pred),
levels=c(0, 1), direction = "<")
# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter
plot(pROC::roc(response = ifelse(selectedDf$Class==T, 1, 0),
predictor = round(xgb_m$pred),
levels=c(0, 1), direction = "<"),
legacy.axes = TRUE,
main="ROC Curve",
lwd=1.5)

Plot feature importance
yAxis <- list(
title = 'Importance',
range=c(0.0, 1.0)
)
xAxis <- list(
title = ''
)
importanceDf$Feature <- factor(importanceDf$Feature, levels = importanceDf[order(-Gain),]$Feature)
fig_Importance <- plot_ly(importanceDf, x = ~Feature, y = ~Gain, type = 'bar', name = 'Gain', width=600) %>%
add_trace(y = ~Cover, name = 'Cover') %>%
add_trace(y = ~Frequency, name = 'Frequency') %>%
layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', title="Feature Importance") %>%
config(.Last.value, mathjax = 'cdn')
htmltools::tagList(fig_Importance)
actualCluster <- data.frame(cbind(as.character(combinedDf$Subject), selectedDf$Class))
names(actualCluster) <- c("Subject", "Class")
actualCluster
# actualCluster[order(Class),]
clusteringDf <- selectedDf %>% select(importanceDf$Feature[1:4])
rownames(clusteringDf) <- paste0(combinedDf$Subject)
fit <- kmeans(clusteringDf, 3)
clusplot(clusteringDf, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)

---
title: "R Notebook"
output: html_notebook
---

```{r}
source('../settings/settings.R')
source('commonFunctions.R')
```

```{r}
inputFileDrive1 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=1, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive2 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=2, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive3 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=3, distPrev=DISTANCE_PREV, distNext=DISTANCE_NEXT))
inputFileDrive4 <- str_interp("../data/processed/analysis/TT1_Drive_${drive}_PP_${distPrev}m_${distNext}m.csv", list(drive=4, distPrev=30, distNext=30))

drive1 <- read.csv(inputFileDrive1)
drive2 <- read.csv(inputFileDrive2)
drive3 <- read.csv(inputFileDrive3)

drive4 <- read.csv(inputFileDrive4, stringsAsFactors = T)
```

```{r}
set.seed(43)
combinedDf <- cbind(drive4, 
                    drive1$MeanPP_Seg0, 
                    drive2$MeanPP, drive3$MeanPP,
                    drive2$StdPP, drive3$StdPP,
                    drive2$MeanPP_SegMax, drive3$MeanPP_SegMax, 
                    drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
                    drive2$StdPP_SegMax, drive3$StdPP_SegMax, 
                    drive2$StdPP_Seg0, drive3$StdPP_Seg0,
                    drive2$MeanPP_AccHigh, drive3$MeanPP_AccHigh,
                    drive2$X.MeanPP_AccLow, drive3$X.MeanPP_AccLow,
                    drive2$StdPP_AccHigh, drive3$StdPP_AccHigh,
                    drive2$StdPP_AccLow, drive3$StdPP_AccLow
                  )
names(combinedDf) <- c(names(drive4), 
                       "PP_Dev_1_Turning",
                       "PP_Dev_2", "PP_Dev_3", 
                       "Std_PP_2", "Std_PP_3",
                       "PP_Dev_2_Straight", "PP_Dev_3_Straight", 
                       "PP_Dev_2_Turning", "PP_Dev_3_Turning", 
                       "Std_PP_2_Straight", "Std_PP_3_Straight", 
                       "Std_PP_2_Turning", "Std_PP_3_Turning",
                       "Mean_PP_2_AccHigh", "Mean_PP_3_AccHigh",
                       "Mean_PP_2_AccLow", "Mean_PP_3_AccLow",
                       "Std_PP_2_AccHigh", "Std_PP_3_AccHigh",
                       "Std_PP_2_AccLow", "Std_PP_3_AccLow"
                       )

combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))
combinedDf$ActivityEncoded <- factor(ifelse(combinedDf$Activity == "NO", "1", ifelse(combinedDf$Activity == "C", "2", "3")))

# combinedDf$PP_Dev_2_Turning <- ifelse(combinedDf$PP_Dev_2_Turning > 0, combinedDf$PP_Dev_2_Turning, combinedDf$PP_Dev_2_Straight)
```

```{r}
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]

combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
```

```{r}
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE_PRIOR <- list(color='green')
COLOR_FAILURE <- list(color='red')
COLOR_COGNITIVE_ACC <- list(color='rgb(158,202,225)')
COLOR_MOTORIC_ACC <- list(color='rgb(58,200,225)')

bargap <- 6
yAxis <- list(
  title = "Arousal ΔPP [ln°C²]",
  range=c(-0.2, 0.6)
)

# Apply Otsu algorithm to select threshold
ppDev <- combinedDf$PP_After # PP_Dev
ppDevArray <- matrix(ppDev, nrow = 1,ncol = length(ppDev))
  
THRESHOLD_MILD = otsu(ppDevArray, range=c(min(ppDev), max(ppDev))) # Expected Threshold > 0.042
print(paste0('Threshold: ', THRESHOLD_MILD))

MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
```

```{r, warning=F}
xAxis = list(
  title = "Subject",
  ticktext = combinedDf_NoStressor$Subject, 
  tickvals = seq(1, bargap * nrow(combinedDf_NoStressor), by=bargap),
  tickmode = "array"
)
combinedDf_NoStressor$SubjectLevel <- seq(1, bargap * nrow(combinedDf_NoStressor), by=bargap)
      
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~SubjectLevel, width=1000) %>%
  # add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  # add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  # add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>% 
  # add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>% 
  
  # add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  # add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>% 
  # add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
  
  # add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
  add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>% 
  add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F) 

fig_NoStressor <- fig_NoStressor %>% config(mathjax = 'cdn')

htmltools::tagList(fig_NoStressor)
```

```{r, warning=F}
xAxis = list(
  title = "Subject",
  ticktext = combinedDf_Cognitive$Subject, 
  tickvals = seq(1, bargap * nrow(combinedDf_Cognitive), by=bargap),
  tickmode = "array"
)
combinedDf_Cognitive$SubjectLevel <- seq(1, bargap * nrow(combinedDf_Cognitive), by=bargap)

fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~SubjectLevel, width=1000) %>%
  # add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  # add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  # add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>% 
  # add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>% 
  
  # add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  # add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>% 
  # add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
  
  # add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
  add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>% 
  add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F)

htmltools::tagList(fig_Cognitive)
```



```{r, warning=F}
xAxis = list(
  title = "Subject",
  ticktext = combinedDf_Motoric$Subject, 
  tickvals = seq(1, bargap * nrow(combinedDf_Motoric), by=bargap),
  tickmode = "array"
)
combinedDf_Motoric$SubjectLevel <- seq(1, bargap * nrow(combinedDf_Motoric), by=bargap)

fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~SubjectLevel, width=1000) %>%
  # add_trace(y = ~PP_Dev_2_Straight, name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  # add_trace(y = ~PP_Dev_1_Turning, name = 'Normal - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  # add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(type="bar", y = ~Mean_PP_2_AccHigh, width=1.58, name = 'ΔPP after HA in CD', marker=COLOR_COGNITIVE_ACC) %>% 
  # add_trace(y = ~Mean_PP_2_AccLow, name = 'Coginitive - Mean PP (Low Accel.)', marker=COLOR_ACC) %>% 
  
  # add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  # add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(type="bar", y = ~Mean_PP_3_AccHigh, width=1.58, name = "ΔPP after HA in MD", marker=COLOR_MOTORIC_ACC) %>% 
  # add_trace(y = ~Mean_PP_3_AccLow, name = 'Motoric - Mean PP (Low Accel.)', marker=COLOR_ACC) %>%
  
  # add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
  add_trace(type="bar", y = ~PP_After, width=1.58, name = 'ΔPP after the catastrophic event', marker=COLOR_FAILURE) %>% 
  add_segments(x=-5, xend=bargap * nrow(combinedDf_NoStressor), y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Otsu Threshold",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', bargap=5, title=F)

htmltools::tagList(fig_Motoric)
```


```{r}
library(nlme)
combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)
combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
```

### Extract data for important features
```{r}
importantFeaturesDf <- combinedDf %>% select(Subject, Std_PP_3, PP_Dev_2_Turning, Activity, PP_Dev, PP_Dev_Group)
```

# Linear model with all variables
```{r}
linearModel1 <- lm(PP_After ~ 
              + PP_Dev_2_Straight
              + PP_Dev_3_Straight
              + PP_Dev_2_Turning
              + PP_Dev_3_Turning
              + Std_PP_2_Straight
              + Std_PP_3_Straight
              + Std_PP_2_Turning
              + Std_PP_3_Turning
              + PP_Prior
              + factor(ActivityEncoded), 
            data=combinedDf)

# anova(model)
summary(linearModel1)
plot(linearModel1)
```

```{r}
linearModel1 <- lm(PP_After ~ 
                Mean_PP_2_AccHigh
              + Mean_PP_2_AccLow
              + Mean_PP_3_AccHigh
              + Mean_PP_3_AccLow
              + Std_PP_2_AccHigh
              + Std_PP_2_AccLow
              + Std_PP_3_AccHigh
              + Std_PP_3_AccLow
              # + PP_Prior
              + factor(ActivityEncoded), 
            data=combinedDf)

# anova(model)
summary(linearModel1)
plot(linearModel1)
```

## With Prior
```{r}
linearModelWPrior <- lm(PP_After ~ 
                Mean_PP_2_AccHigh
              + Mean_PP_2_AccLow
              + Mean_PP_3_AccHigh
              + Mean_PP_3_AccLow
              + Std_PP_2_AccHigh
              + Std_PP_2_AccLow
              + Std_PP_3_AccHigh
              + Std_PP_3_AccLow
              + PP_Prior
              + factor(ActivityEncoded), 
            data=combinedDf)

# anova(model)
summary(linearModelWPrior)
plot(linearModelWPrior)
```

```{r}
linearModel3 <- lm(PP_After ~ 
                Mean_PP_2_AccHigh
              + Mean_PP_2_AccLow
              + Mean_PP_3_AccHigh
              + Mean_PP_3_AccLow
              # + PP_Prior
              + factor(ActivityEncoded), 
            data=combinedDf)

# anova(model)
summary(linearModel3)
plot(linearModel3)
```


```{r}
# Export the anova table
library(xtable)
lmCoeffs <- summary(linearModel3)$coefficients
lmAnova <- anova(linearModel3)

print(xtable(lmCoeffs, digits=c(0,5,5,5,5)))
print(xtable(lmAnova), digits=c(0,5,5,5,5))

```


```{r}
ppAfter <- combinedDf$PP_After
ppAfterArray <- matrix(ppAfter, nrow = 1,ncol = length(ppAfter))
  
thresholdPPAfter <- otsu(ppAfterArray, range=c(min(ppAfter), max(ppAfter))) # Expected Threshold > 0.042
print(paste0('Threshold: ', thresholdPPAfter))

selectedDf <- combinedDf %>% select(
                  "Subject", "Activity", "PP_After", "PP_Prior",
                  "Mean_PP_2_AccHigh", "Mean_PP_3_AccHigh",
                  "Mean_PP_2_AccLow", "Mean_PP_3_AccLow",
                  "Std_PP_2_AccHigh", "Std_PP_3_AccHigh",
                  "Std_PP_2_AccLow", "Std_PP_3_AccLow")

selectedDf$Subject <- NULL
selectedDf$Activity_NO <- ifelse(selectedDf$Activity == "NO", 1, 0)
selectedDf$Activity_C <- ifelse(selectedDf$Activity == "C", 1, 0)
selectedDf$Activity_M <- ifelse(selectedDf$Activity == "M", 1, 0)
selectedDf$Activity <- NULL

# selectedDf$PP_Dev_1_Turning <- NULL
# selectedDf$Std_PP_2_Straight <- NULL
# selectedDf$Std_PP_2_Turning <- NULL
# selectedDf$Std_PP_3_Straight <- NULL
# selectedDf$Std_PP_3_Turning <- NULL
# 
# # According to Linear model
# selectedDf$PP_Dev_2_Straight <- abs(selectedDf$PP_Dev_2_Straight)
# selectedDf$PP_Dev_3_Straight <- abs(selectedDf$PP_Dev_3_Straight)
# selectedDf$PP_Dev_2_Turning <- abs(selectedDf$PP_Dev_2_Turning)
# selectedDf$PP_Dev_3_Turning <- abs(selectedDf$PP_Dev_3_Turning)
# selectedDf$PP_Prior <- abs(selectedDf$PP_Prior) # NULL

selectedDf$Class <- ifelse(selectedDf$PP_After >= thresholdPPAfter, T, F)
selectedDf$PP_After <- NULL

print(names(selectedDf))
```

```{r}
# library(mefa)
# combinedDf <- rep(combinedDf, 10) 
```

```{r}
set.seed(43)
n_folds <- 3
params <- param <- list(objective       = "binary:logistic", 
               booster          = "gbtree",
               eval_metric      = "auc",
               eta              = 0.1,
               max_depth        = 10,
               alpha            = 1,
               lambda           = 0,
               gamma            = 0.45,
               min_child_weight = 0.3,
               subsample        = 0.5,
               colsample_bytree = 1)
           
# XGBoost Model         
xgb_m <- xgb.cv(   params               = param,
                  data = as.matrix(selectedDf %>% select(-Class)) ,
                  label =  selectedDf$Class,
                  nrounds             = 100,
                  verbose             = F,
                  prediction          = T,
                  maximize            = F, # Change this value to F will help to run with more itineration
                  nfold               = n_folds,
                  metrics             = c("auc", "error"),
                  early_stopping_rounds = 50,
                  stratified            = T,
                  scale_pos_weight      = 1)

# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]

```

## Performance Metrics
```{r}
# Prediction
selectedDf$clsPred <- round(xgb_m$pred)

computePerformanceResults <- function(sdat){
  sdat = sdat[complete.cases(sdat),]
  acc = sum(sdat[,1] == sdat[,2])/nrow(sdat)
  conf_mat = table(sdat)
  specif = conf_mat[1,1]/sum(conf_mat[,1])
  sensiv = conf_mat[2,2]/sum(conf_mat[,2])
  preci =  conf_mat[2,2]/sum(conf_mat[2,])
  npv =    conf_mat[1,1]/sum(conf_mat[1,])
  return(c(acc,specif,sensiv,preci,npv))
}

# Get average performance
performance <- computePerformanceResults(selectedDf %>% select(Class, clsPred))
acc <- performance[1]
prec <- performance[4]
recall <- performance[3]
spec <- performance[2]
npv <- performance[5]
f1 <- (2 * recall * prec) / (recall + prec)
auc <- as.numeric(xgb_m$evaluation_log[xgb_m$best_iteration, "test_auc_mean"])

print(paste("Accuracy=", round(acc, 2)))
print(paste("Precision=", round(prec, 2)))
print(paste("Recall=", round(recall, 2)))
print(paste("Specificity=", round(spec, 2)))
print(paste("NPV=", round(npv, 2)))
print(paste("F1=", round(f1, 2)))
print(paste("AUC=", round(auc, 2)))
```

```{r}
# Importance
bst <- xgboost(   params               = param,
                  data = as.matrix(selectedDf %>% select(-c(Class, clsPred))) ,
                  label =  selectedDf$Class,
                  nrounds             = 100,
                  verbose             = F,
                  prediction          = T,
                  maximize            = F, # Change this value to F will help to run with more itineration
                  nfold               = n_folds,
                  metrics             = c("auc", "error"),
                  early_stopping_rounds = 50,
                  stratified            = T,
                  scale_pos_weight      = 1)
importanceDf <- xgb.importance(colnames(selectedDf %>% select(-c(Class, clsPred))), model = bst)
print(importanceDf)
```

```{r}
library(pROC)

dfROC <- pROC::roc(response = ifelse(selectedDf$Class==T, 1, 0),
               predictor = round(xgb_m$pred),
               levels=c(0, 1), direction = "<")

# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter 

plot(pROC::roc(response = ifelse(selectedDf$Class==T, 1, 0),
               predictor = round(xgb_m$pred),
               levels=c(0, 1), direction = "<"), 
     legacy.axes = TRUE,
     main="ROC Curve", 
     lwd=1.5) 
```


### Plot feature importance
```{r}
yAxis <- list(
  title = 'Importance',
  range=c(0.0, 1.0)
)
xAxis <- list(
  title = ''
)

importanceDf$Feature <- factor(importanceDf$Feature, levels = importanceDf[order(-Gain),]$Feature)
fig_Importance <- plot_ly(importanceDf, x = ~Feature, y = ~Gain, type = 'bar', name = 'Gain', width=600) %>%
  add_trace(y = ~Cover, name = 'Cover') %>% 
  add_trace(y = ~Frequency, name = 'Frequency') %>% 
  layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', title="Feature Importance") %>% 
  config(.Last.value, mathjax = 'cdn')

htmltools::tagList(fig_Importance)
```

```{r}
actualCluster <- data.frame(cbind(as.character(combinedDf$Subject), selectedDf$Class))
names(actualCluster) <- c("Subject", "Class")
actualCluster
# actualCluster[order(Class),]
```

```{r}
clusteringDf <- selectedDf %>% select(importanceDf$Feature[1:4])
rownames(clusteringDf) <- paste0(combinedDf$Subject)
fit <- kmeans(clusteringDf, 3)
clusplot(clusteringDf, fit$cluster, color=TRUE, shade=TRUE,
   labels=2, lines=0)
```

